Job Title : Agentic AI / Semantic Solutions Architect

Location : Atlanta, Georgia, USA (Hybrid on site)

Experience : 13+Years

Role Overview

We are seeking a highly skilled Agentic AI / Semantic Solutions Architect to design and prototype advanced agent-layer architectures that operate on enterprise semantic data platforms. This role sits at the intersection of LLM orchestration, knowledge graphs, and semantic data modeling, focusing on building POC-level intelligent agent solutions rather than production-scale systems.

The ideal candidate will have deep expertise in agent-based AI systems, GraphRAG architectures, and context engineering, with the ability to design frameworks where autonomous agents can effectively interpret and reason over structured knowledge.

Key Responsibilities

• Architect and design agentic AI workflows that consume outputs from semantic layers, including knowledge graphs, ontologies, and metadata catalogs

• Develop and prototype GraphRAG pipelines that combine graph traversal with vector-based retrieval for accurate, domain-grounded responses

• Define and implement context engineering strategies, including metadata injection, chunking, and semantic optimization for LLM prompts

• Design and build Model Context Protocol (MCP) server patterns to enable seamless interaction between agents and semantic data systems

• Develop LLM orchestration workflows using frameworks such as LangChain, LangGraph, LlamaIndex, or AutoGen

• Build pipelines for automated metadata extraction and semantic tagging using NLP and LLM-based approaches

• Collaborate with Semantic Data Architects to ensure ontologies and graph structures are optimized for agent traversal and querying

• Prototype agent-based solutions for business use cases such as:

o Credit risk analysis

o Customer data onboarding workflows

Mandatory Skills

• Strong expertise in Agentic AI architecture (multi-agent systems, tool usage, planning loops)

• Hands-on experience with GraphRAG design (hybrid graph + vector retrieval systems)

• Experience in LLM orchestration frameworks:

o LangChain, LangGraph, LlamaIndex, or AutoGen

• Deep understanding of context engineering techniques (chunking, windowing, semantic compression)

• Experience designing and integrating Model Context Protocol (MCP)

• Strong knowledge of semantic systems such as:

o Knowledge graphs

o Ontologies

o Metadata-driven architectures

Nice to Have Skills

• Experience with Google Vertex AI (Agent Builder / Search)

• Knowledge of GCP Spanner Graph

• Familiarity with metadata platforms like Collibra or Google Dataplex

• Experience with vector databases:

o Pinecone, Weaviate, pgvector, Vertex AI Vector Search

• Prior experience in regulated domains such as financial services or legal systems